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initial project setup with README and ignore

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Suriya
2026-04-08 15:13:42 +05:30
commit 2d5688cb35
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app/config/__init__.py Normal file
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"""Configuration package for mobile delivery optimization."""

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app/config/dynamic_config.py Normal file
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"""
Dynamic Configuration - rider-api
Replaces all hardcoded hyperparameters with DB-backed values.
The ML hypertuner writes optimal values here; services read from here.
Fallback: If DB is unavailable or no tuned values exist, defaults are used.
This means zero risk - the system works day 1 with no data.
"""
import json
import logging
import os
import sqlite3
from datetime import datetime
from typing import Any, Dict, Optional
logger = logging.getLogger(__name__)
# --- DB Path ------------------------------------------------------------------
_DB_PATH = os.getenv("ML_DB_PATH", "ml_data/ml_store.db")
# --- Hard Defaults (What the system used before ML) ---------------------------
DEFAULTS: Dict[str, Any] = {
# System Strategy / Prompt
"ml_strategy": "balanced",
# AssignmentService
"max_pickup_distance_km": 10.0,
"max_kitchen_distance_km": 3.0,
"max_orders_per_rider": 12,
"ideal_load": 6,
"workload_balance_threshold": 0.7,
"workload_penalty_weight": 100.0,
"distance_penalty_weight": 2.0,
"preference_bonus": -15.0,
"home_zone_bonus_4km": -3.0,
"home_zone_bonus_2km": -5.0,
"emergency_load_penalty": 3.0, # km penalty per order in emergency assign
# RouteOptimizer
"search_time_limit_seconds": 5,
"avg_speed_kmh": 18.0,
"road_factor": 1.3,
# ClusteringService
"cluster_radius_km": 3.0,
# KalmanFilter
"kalman_process_noise": 1e-4,
"kalman_measurement_noise": 0.01,
# RealisticETACalculator
"eta_pickup_time_min": 3.0,
"eta_delivery_time_min": 4.0,
"eta_navigation_buffer_min": 1.5,
"eta_short_trip_factor": 0.8, # speed multiplier for dist < 2km
"eta_long_trip_factor": 1.1, # speed multiplier for dist > 8km
}
class DynamicConfig:
"""
Thread-safe, DB-backed configuration store.
Usage:
cfg = DynamicConfig()
max_dist = cfg.get("max_pickup_distance_km")
all_params = cfg.get_all()
"""
_instance: Optional["DynamicConfig"] = None
def __new__(cls) -> "DynamicConfig":
"""Singleton - one config per process."""
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._initialized = False
return cls._instance
def __init__(self):
if self._initialized:
return
self._initialized = True
self._cache: Dict[str, Any] = {}
self._last_loaded: Optional[datetime] = None
self._ensure_db()
self._load()
# --------------------------------------------------------------------------
# Public API
# --------------------------------------------------------------------------
def get(self, key: str, default: Any = None) -> Any:
"""Get a config value. Returns ML-tuned value if available, else default."""
self._maybe_reload()
val = self._cache.get(key)
if val is not None:
return val
fallback = default if default is not None else DEFAULTS.get(key)
return fallback
def get_all(self) -> Dict[str, Any]:
"""Return all current config values (ML-tuned + defaults for missing keys)."""
self._maybe_reload()
result = dict(DEFAULTS)
result.update(self._cache)
return result
def set(self, key: str, value: Any, source: str = "manual") -> None:
"""Write a config value to DB (used by hypertuner)."""
try:
os.makedirs(os.path.dirname(_DB_PATH) or ".", exist_ok=True)
conn = sqlite3.connect(_DB_PATH)
conn.execute("""
INSERT INTO dynamic_config (key, value, source, updated_at)
VALUES (?, ?, ?, ?)
ON CONFLICT(key) DO UPDATE SET
value=excluded.value,
source=excluded.source,
updated_at=excluded.updated_at
""", (key, json.dumps(value), source, datetime.utcnow().isoformat()))
conn.commit()
conn.close()
self._cache[key] = value
logger.info(f"[DynamicConfig] Set {key}={value} (source={source})")
except Exception as e:
logger.error(f"[DynamicConfig] Failed to set {key}: {e}")
def set_bulk(self, params: Dict[str, Any], source: str = "ml_hypertuner") -> None:
"""Write multiple config values at once (called after each Optuna study)."""
for key, value in params.items():
self.set(key, value, source=source)
logger.info(f"[DynamicConfig] Bulk update: {len(params)} params from {source}")
def reset_to_defaults(self) -> None:
"""Wipe all ML-tuned values, revert to hardcoded defaults."""
try:
conn = sqlite3.connect(_DB_PATH)
conn.execute("DELETE FROM dynamic_config")
conn.commit()
conn.close()
self._cache.clear()
logger.warning("[DynamicConfig] Reset to factory defaults.")
except Exception as e:
logger.error(f"[DynamicConfig] Reset failed: {e}")
# --------------------------------------------------------------------------
# Internal
# --------------------------------------------------------------------------
def _ensure_db(self) -> None:
try:
os.makedirs(os.path.dirname(_DB_PATH) or ".", exist_ok=True)
conn = sqlite3.connect(_DB_PATH)
conn.execute("""
CREATE TABLE IF NOT EXISTS dynamic_config (
key TEXT PRIMARY KEY,
value TEXT NOT NULL,
source TEXT DEFAULT 'manual',
updated_at TEXT
)
""")
conn.commit()
conn.close()
except Exception as e:
logger.error(f"[DynamicConfig] DB init failed: {e}")
def _load(self) -> None:
try:
conn = sqlite3.connect(_DB_PATH)
rows = conn.execute("SELECT key, value FROM dynamic_config").fetchall()
conn.close()
self._cache = {}
for key, raw in rows:
try:
self._cache[key] = json.loads(raw)
except Exception:
self._cache[key] = raw
self._last_loaded = datetime.utcnow()
if self._cache:
logger.info(f"[DynamicConfig] Loaded {len(self._cache)} ML-tuned params from DB")
except Exception as e:
logger.warning(f"[DynamicConfig] Could not load from DB (using defaults): {e}")
self._cache = {}
def _maybe_reload(self, interval_seconds: int = 300) -> None:
"""Reload from DB every 5 minutes - picks up new tuned params without restart."""
if self._last_loaded is None:
self._load()
return
delta = (datetime.utcnow() - self._last_loaded).total_seconds()
if delta > interval_seconds:
self._load()
# --- Module-level convenience singleton ---------------------------------------
_cfg = DynamicConfig()
def get_config() -> DynamicConfig:
"""Get the global DynamicConfig singleton."""
return _cfg

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app/config/mobile_config.py Normal file
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"""Mobile-specific configuration for delivery route optimization."""
# Mobile optimization settings
MOBILE_CONFIG = {
"default_algorithm": "greedy",
"max_deliveries": 100,
"timeout_seconds": 5,
"response_compression": True,
"performance_monitoring": True,
"mobile_headers": True
}
# Performance targets for mobile
PERFORMANCE_TARGETS = {
"greedy_algorithm": {
"max_response_time": 0.1, # 100ms
"max_deliveries": 50,
"description": "Ultra-fast for real-time mobile apps"
},
"tsp_algorithm": {
"max_response_time": 3.0, # 3 seconds
"max_deliveries": 30,
"description": "Optimal but slower, good for planning"
}
}
# Mobile app recommendations
MOBILE_RECOMMENDATIONS = {
"real_time_delivery": "greedy",
"route_planning": "tsp",
"large_batches": "greedy",
"cost_optimization": "tsp"
}

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app/config/rider_preferences.py Normal file
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"""
Rider Preferred Kitchens Configuration
Mapping of Rider ID (int) to list of preferred Kitchen names (str).
Updated based on Deployment Plan.
"""
RIDER_PREFERRED_KITCHENS = {
# 1. VIVEK ANANDHAN - LOCAL, RS PURAM TO SELVAPURAM
1116: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
# 2. NARAYANASAMY - VENGATAPURAM, VADAVALI, TADAGAM ROAD
1096: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
# 3. VARUN EDWARD - GN MILLS, KAVUNDAMPALAYAM, THUDIYALUR
897: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
# 4. JAYASABAESH - GANAPTHY
950: ["Daily grubs nandhini", "Vidhya kitchen"],
# 5. TAMILALAHZAN - GANDHIMA NAGAR
1114: ["Daily grubs nandhini", "Vidhya kitchen"],
# 6. RAJAN - PEELAMDU
883: ["Daily grubs nandhini", "Vidhya kitchen"],
# 7. MUTHURAJ - RAMANATHAPURAM TO SAIBABACOLONY
1272: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen", "Daily grubs nandhini", "Vidhya kitchen"],
# 8. MANIKANDAN - SINGNALLUR
753: ["Daily grubs nandhini", "Vidhya kitchen"],
# 9. TACHANAMOORTHI - KOVAI PUTHUR TO KAVUNDAMPALAYAM
1271: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"],
1133: ["Daily grubs(jayanthi kitchen)", "Bhuvaneshwari kitchen", "Hilda kitchen", "Kalpana kitchen"], # Active ID
}
# Anchor Coordinates for Riders (Based on Area Name)
# Used as fallback if GPS is missing, or to bias assignment to their Home Zone.
RIDER_HOME_LOCATIONS = {
1116: (11.0067, 76.9558), # VIVEK ANANDAN: RS PURAM
1096: (11.0450, 76.9000), # NARAYANASAMY: VADAVALI
897: (11.0430, 76.9380), # VARUN EDWARD: KAVUNDAMPALAYAM
950: (11.0330, 76.9800), # JAYASABESH: GANAPATHY
1114: (11.0450, 77.0000), # TAMILAZHAGAN: GANDHIMA NAGAR
883: (11.0200, 77.0000), # RAJAN: PEELAMEDU
1272: (10.9950, 77.0000), # MUTHURAJA: RAMANATHAPURAM
753: (11.0000, 77.0300), # MANIKANDAN: SINGANALLUR
1271: (10.9500, 76.9600), # THATCHINAMOORTHI: KOVAI PUDUR
}